Introduction

In this code sentiment analysis is conducted using the Sentida package. Additionally, the development in sentiment over time is plotted using the gganimatepackage.

# load packages 
library(pacman)
p_load(rlist, Sentida, ggplot2, tidyverse, gganimate, gifski)

# set theme 
theme_set(theme_bw()) 

# load data
tweets_before <- read.csv("tweets_before.csv")
tweets_after <- read.csv("tweets_after.csv")

Creating functions for data cleaning and summary

  1. The sentiment_func calculates the total sentiment per tweet and add this value in a new column.

  2. The date_func cleans the date column (created_at) and make it into date format, so it can be used in the gganimate plots and returns a dataframe where the column has been cleaned.

  3. The mean_sentiment_pr_day calculates the mean sentiment per unique date in the dataset

# sentiment function 
sentiment_func <- function(df){
  sentiment <- c()
  for (i in df$full_text) {
    l = sentida(i, output = "total")
    sentiment = list.append(sentiment, l)
  }
  df <- cbind(df,sentiment)
  return(df)
}


# date format function
date_func <- function(df){
  df$created_at <- str_replace_all(df$created_at, "\\ .*", "")
  df$created_at <- str_replace_all(df$created_at, "2020-", "20-")
  df$created_at <-  as.Date(df$created_at)
  return(df)
  }

# get average sentiment pr unique dates 
mean_sentiment_pr_day =
  function(df){
   summary = df %>% group_by(created_at) %>% summarise(mean_sentiment=mean(sentiment),day=min(created_at)) %>% select(-created_at)
  return(summary)
   }

Getting only tweets with the hashtags #dkgreen and #dkpol (before and after the chosen date 5/5)

#dkgreen before
dkgreen_before <- filter(tweets_before, dkgreen=="True")

#dkgreen after
dkgreen_after <- filter(tweets_after, dkgreen=="True")

#dkpol before
dkpol_before <- filter(tweets_before, dkpol=="True")

#dkpol after
dkpol_after <- filter(tweets_after, dkpol=="True")

Applying functions to the four datasets

dkgreen_before <- dkgreen_before %>% sentiment_func() %>% date_func() 

dkgreen_after <- dkgreen_after %>% sentiment_func() %>% date_func()

dkpol_before <- dkpol_before %>% sentiment_func() %>% date_func()

dkpol_after <- dkpol_after %>% sentiment_func() %>% date_func()

Getting mean sentiment scores per unique date in a summary dataframe

dkgreen_before_sum <- dkgreen_before %>% mean_sentiment_pr_day()
## `summarise()` ungrouping output (override with `.groups` argument)
dkgreen_after_sum <- dkgreen_after %>% mean_sentiment_pr_day()
## `summarise()` ungrouping output (override with `.groups` argument)
dkpol_before_sum <- dkpol_before %>% mean_sentiment_pr_day()
## `summarise()` ungrouping output (override with `.groups` argument)
dkpol_after_sum <- dkpol_after %>% mean_sentiment_pr_day()
## `summarise()` ungrouping output (override with `.groups` argument)

Making animated plots